% Built off of material by Chris Krogslund

% note to C. Paciorek: make of this is crashing in R 3.2.1 on smeagol but not R 3.2.0 on arwen

By way of introduction…

3 main facilities for producing graphics in R: base, lattice, and ggplot2
In practice, these facilities are grouped into two camps: “basic” and “advanced”
A better formulation: quick/dirty v. involved/fancy

The data

All Summer Olympic medalists from 1896-2008
Variables include name, gender, country, sporting event, and type of medal won

For more info, see The Guardian Datablog Olympic medal winners: every one since 1896 as open data

head(medals)

##     City    Sport Discipline               Athlete NOC Gender
## 1 Athens Aquatics   Swimming         HAJOS, Alfred HUN    Men
## 2 Athens Aquatics   Swimming      HERSCHMANN, Otto AUT    Men
## 3 Athens Aquatics   Swimming     DRIVAS, Dimitrios GRE    Men
## 4 Athens Aquatics   Swimming    MALOKINIS, Ioannis GRE    Men
## 5 Athens Aquatics   Swimming    CHASAPIS, Spiridon GRE    Men
## 6 Athens Aquatics   Swimming CHOROPHAS, Efstathios GRE    Men
##                        Event Event_gender  Medal Year Country
## 1             100m freestyle            M   Gold 1896 Hungary
## 2             100m freestyle            M Silver 1896 Austria
## 3 100m freestyle for sailors            M Bronze 1896  Greece
## 4 100m freestyle for sailors            M   Gold 1896  Greece
## 5 100m freestyle for sailors            M Silver 1896  Greece
## 6            1200m freestyle            M Bronze 1896  Greece

# dplyr and tidyr refresher - we want to count the medals of each type, by country and by year
medal_counts <- medals %>% group_by(Medal, Year, Country) %>% summarise(count = n())
head(medal_counts)

## Source: local data frame [6 x 4]
## Groups: Medal, Year [1]
## 
##    Medal  Year Country count
##    <chr> <int>   <chr> <int>
## 1 Bronze  1896 Austria     2
## 2 Bronze  1896 Denmark     3
## 3 Bronze  1896  France     2
## 4 Bronze  1896 Germany     2
## 5 Bronze  1896  Greece    22
## 6 Bronze  1896 Hungary     3

medal_counts_wide <- medal_counts %>% spread(key = Medal, value = count) %>%
  ungroup() %>%
  mutate(Bronze = ifelse(is.na(Bronze), 0, Bronze)) %>% 
  mutate(Silver = ifelse(is.na(Silver), 0, Silver)) %>% 
  mutate(Gold = ifelse(is.na(Gold), 0, Gold)) 
head(medal_counts_wide)

## # A tibble: 6 x 5
##    Year   Country Bronze  Gold Silver
##   <int>     <chr>  <dbl> <dbl>  <dbl>
## 1  1896 Australia      0     2      0
## 2  1896   Austria      2     2      1
## 3  1896   Denmark      3     1      2
## 4  1896    France      2     5      4
## 5  1896   Germany      2    26      5
## 6  1896    Greece     22    10     20

medal_lm <- lm(Gold ~ Bronze + Silver, data = medal_counts_wide)
gold_medal_counts <- medal_counts %>% filter(Medal == "Gold")
usa_gold_medals <- gold_medal_counts %>% 
  filter(Country == "United States")

Base plot

TO DO ## Default plot

plot(gold_medal_counts$Year, gold_medal_counts$count, main = "Gold Medal Counts")

plot(usa_gold_medals$Year, usa_gold_medals$count, type = "l", main = "USA Gold Medals")

Other plot types

hist(gold_medal_counts$count)

plot(density(gold_medal_counts$count))

barplot(usa_gold_medals$count, width = 4, names.arg = usa_gold_medals$Year, main = "USA Gold Medals")

mosaicplot(Year~Medal, medal_counts)

boxplot(count~Country, medal_counts %>% filter(Country %in% c("United States", "Canada", "Mexico")))

Object-oriented plots

Base graphics often recognizes the object type and will implement specific plot methods
lattice and ggplot2 generally don’t exhibit this sort of behavior

plot(medal_counts_wide %>% select(-Country)) # Calls plotting method for class of the dataset ("data.frame")

plot(medal_lm, which=1:2) # Calls plotting method for class of medal_lm object ("lm"), print first two plots only

Pros/cons

Good for exploratory data analysis and sanity checks
Syntax is inconsistent across functions: some take x,y while others take formulas
Defaults plotting parameters are ugly and it can be difficult to customize

lattice and ggplot2

lattice is

faster (though only noticeable over many and large plots)
simpler (at first)
better at trellis graphs
able to do 3d graphs

ggplot2 is

generally more elegant
more syntactically logical (and therefore simpler, once you learn it)
better at grouping
able to interface with maps

Basic usage: lattice

The general call for lattice graphics looks something like this:

graph_type(formula, data=, [options])

The specifics of the formula differ for each graph type, but the general format is straightforward

y             # Show the distribution of y

y~x           # Show the relationship between x and y 

y~x|A         # Show the relationship between x and y conditional on the values of A

y~x|A*B       # Show the relationship between x and y conditional on the combinations of A and B

z~y*x         # Show the 3D relationship between x, y, and z

Basic usage: ggplot2

The general call for ggplot2 graphics looks something like this:

ggplot(data=, aes(x=,y=, [options])) + geom_xxxx() + ... + ... + ...

Note that ggplot2 graphs in layers in a continuing call (hence the endless +…+…+…), which really makes the extra layer part of the call

...+geom_xxxx(data=, aes(x=,y=,[options]),[options])+...+...+...

You can see the layering effect by comparing the same graph with different colors for each layer

ggplot(data=data, aes(x=year, y=realgdpgr)) + geom_point(color="black") + geom_point(aes(x=year, y=unemp), color="red")

ggplot(data=data, aes(x=year, y=realgdpgr)) + geom_point(color="red") + geom_point(aes(x=year, y=unemp), color="black")

Comparing lattice and ggplot

Density Plots
Scatter Plots
Line Plots
Bar plots
Box plots
Trellis Plots
Contour Plots
Tile/Image Plots
3D Plots (lattice)
Panel Plots (ggplot2)

lattice v. ggplot2: Densities

ggplot(data=data, aes(x=vturn)) + geom_density() # ggplot2

densityplot(~vturn, data=data) # lattice

lattice v. ggplot2: X-Y scatter plots

ggplot(data=data, aes(x=year, y=outlays)) + geom_point() # ggplot2

xyplot(outlays~year, data=data) # lattice

lattice v. ggplot2: X-Y line plots

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + geom_line() # ggplot2

xyplot(outlays~year, data=data[data$country=="USA",], type="l") # lattice

lattice v. ggplot2: bar plots

# Create data.frame of average growth rates by country over time
growth <- ddply(.data=data, .variables=.(country), summarize, mean=mean(realgdpgr, na.rm=T))

ggplot(data=growth, aes(x=country, y=mean)) + geom_bar(stat="identity") # ggplot2

barchart(mean~country, data=growth) # lattice

lattice v. ggplot2: box plots

ggplot(data=data, aes(x=country, y=outlays)) + geom_boxplot() # ggplot2

bwplot(outlays~country, data=data) # lattice

lattice v. ggplot2: “trellis” plots

ggplot(data=data, aes(x=year, y=outlays)) + geom_point() + facet_wrap(~country) # ggplot2

xyplot(outlays~year|country, data=data) # lattice

lattice v. ggplot2: countour plots

data(volcano) # Load volcano contour data
volcano[1:10, 1:10] # Examine volcano dataset (first 10 rows and columns)

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]  100  100  101  101  101  101  101  100  100   100
##  [2,]  101  101  102  102  102  102  102  101  101   101
##  [3,]  102  102  103  103  103  103  103  102  102   102
##  [4,]  103  103  104  104  104  104  104  103  103   103
##  [5,]  104  104  105  105  105  105  105  104  104   103
##  [6,]  105  105  105  106  106  106  106  105  105   104
##  [7,]  105  106  106  107  107  107  107  106  106   105
##  [8,]  106  107  107  108  108  108  108  107  107   106
##  [9,]  107  108  108  109  109  109  109  108  108   107
## [10,]  108  109  109  110  110  110  110  109  109   108

volcano3d <- melt(volcano) # Use reshape2 package to melt the data
head(volcano3d) # Examine volcano3d dataset (head)

##   Var1 Var2 value
## 1    1    1   100
## 2    2    1   101
## 3    3    1   102
## 4    4    1   103
## 5    5    1   104
## 6    6    1   105

names(volcano3d) <- c("xvar", "yvar", "zvar") # Rename volcano3d columns

ggplot(data=volcano3d, aes(x=xvar, y=yvar, z = zvar)) + geom_contour() # ggplot2

contourplot(zvar~xvar + yvar, data=volcano3d) # lattice

lattice v. ggplot2: tile/image/level plots

ggplot(data=volcano3d, aes(x=xvar, y=yvar, z = zvar)) + geom_tile(aes(fill=zvar)) # ggplot2

levelplot(zvar~xvar + yvar, data=volcano3d) # lattice

lattice: 3D plots

# Create a subset of the dataset containing only data for France
france.data <- data[data$country=="France",]
cloud(outlays~year*realgdpgr, data=france.data)

# Create a subset of the dataset containing only data for Greece, Portugal, Ireland, and Spain
pigs.data <- data[data$country %in% c("Greece", "Portugal", "Ireland", "Spain"),]
cloud(outlays~year*realgdpgr|country, data=pigs.data)

ggplot2: Panel plots

ggplot(data=pigs.data, aes(x=year, y=realgdpgr, color=country)) + geom_line()

lattice v. ggplot2: options [labeling]

ggplot(data=data, aes(x=year, y=outlays))  +  geom_point() + 
xlab(label="Voter Turnout (%)") + ylab(label="Government Outlays") + 
ggtitle(label="Cool Graph") # ggplot2

xyplot(outlays~year, data=data, xlab="Year", ylab="Government Outlays", main
 ="Cool Graph") # lattice

lattice v. ggplot2: options [axis + size scaling]

ggplot(data=data, aes(x=year, y=outlays)) + geom_point() # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(size=3) # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(size=1) # ggplot2

xyplot(outlays~year, data=data) # lattice

xyplot(outlays~year, data=data, cex=2) # lattice

xyplot(outlays~year, data=data, cex=.5) # lattice

lattice v. ggplot2: options [graphical parameters]

Colors

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(color=colors()[145]) # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(color="red") # ggplot2

xyplot(outlays~year, data=data, col=colors()[145]) #lattice

xyplot(outlays~year, data=data, col="red") #lattice

Point Styles and Widths

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape=3) # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape=15) # ggplot2

xyplot(outlays~year, data=data, pch=3) # lattice

xyplot(outlays~year, data=data, pch=15) # lattice

Point Styles and Widths

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape=3) # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape=15) # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape="w") # ggplot2

ggplot(data=data, aes(x=year, y=outlays)) + geom_point(shape="$", size=5) # ggplot2

xyplot(outlays~year, data=data, pch=3) # lattice

xyplot(outlays~year, data=data, pch=15) # lattice

xyplot(outlays~year, data=data, pch="w") # lattice

xyplot(outlays~year, data=data, pch="$", cex=2) # lattice

Line Styles and Widths

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=1) # ggplot2

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=2) # ggplot2

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=3) # ggplot2

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=3, size=1) # ggplot2

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=3, size=1.5) # ggplot2

ggplot(data=data[data$country=="USA",], aes(x=year, y=outlays)) + 
geom_line(linetype=3, size=2) # ggplot2

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=1) # lattice

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=2) # lattice

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=3) # lattice

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=3, lwd=2) # lattice

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=3, lwd=3) # lattice

xyplot(outlays~year, data=data[data$country=="USA",], type="l", lty=3, lwd=4) # lattice

ggplot2 and the Grammar of Graphics

By now, you might be noticing some trends in how these two packages approach graphics
lattice tends to focus on a particular type of graph and how to represent cross-sectional variation by splitting it up into smaller chunks
Becoming a proficient user of lattice requires learning a huge array of graph-specific formulas and options
ggplot2 tries to represent much more of the cross-sectional variation by making use of various “aesthetics”; general approach is based on The Grammar of Graphics

ggplot2 and the Grammar of Graphics

Basic idea is that the visualization of all data requires four items

One or more statistics conveying information about the data (identities, means, medians, etc.)
A coordinate system that differentiates between the intersections of statistics (at most two for ggplot, three for lattice)
Geometries that differentiate between off-coordinate variation in kind
Scales that differentiate between off-coordinate variation in degree

ggplot2 allows the user to manipulate all four of these items

Anatomy of aes()

ggplot(data=, aes(x=, y=, color=, linetype=, shape=, size=))

ggplot2 is optimized for showing variation on all four aesthetic types

# Differences in kind using color
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_line(aes(color=country))

Note what happens when we specify the color parameter outside of the aesthetic operator. ggplot2 views these specifications as invalid graphical parameters.

ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_line(color=country)
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_line(color="country")
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_line(color="red")

# Differences in kind using line types
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_line(aes(linetype=country))

# Differences in kind using point shapes
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point(aes(shape=country))

# Differences in degree using color
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point(aes(color=realgdpgr))

# Differences in degree using point size
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point(aes(size=realgdpgr))

# Multiple non-cartesian aesthetics (differences in kind using color, degree using point size)
ggplot(data=pigs.data, aes(x=year, y=outlays)) + 
  geom_point(aes(color=country,size=realgdpgr))

Fitted lines and curves with ggplot2

ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point()

# Add linear model (lm) smoother
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point() + 
  geom_smooth(method="lm")

# Add local linear model (loess) smoother, span of 0.75 
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point() + 
  geom_smooth(method="loess", span=.75)

# Add local linear model (loess) smoother, span of 0.25 
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point() + 
  geom_smooth(method="loess", span=.25)

# Add linear model (lm) smoother, no standard error shading 
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point() + 
  geom_smooth(method="lm", se=F)

# Add local linear model (loess) smoother, no standard error shading 
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point() + 
  geom_smooth(method="loess", se=F)

# Add a local linear (loess) smoother for each country
ggplot(data=pigs.data, aes(x=year, y=outlays)) + geom_point(aes(color=country)) + 
  geom_smooth(aes(color=country))

# Add a local linear (loess) smoother for each country, no standard error shading
ggplot(data=pigs.data, aes(x=year, y=outlays)) +
  geom_point(aes(color=country, size=realgdpgr)) + 
  geom_smooth(aes(color=country), se=F)

lattice v. ggplot2: tables

Both lattice and ggplot2 graphs can be combined using the grid.arrange() function in the gridExtra package

# Initialize gridExtra library
library(gridExtra)
# Create 3 plots to combine in a table
plot1 <- ggplot(data=pigs.data, aes(x=year, y=outlays, color=)) + 
  geom_line(aes(color=country))
plot2 <- ggplot(data=pigs.data, aes(x=year, y=outlays, linetype=)) + 
  geom_line(aes(linetype=country))
plot3 <- ggplot(data=pigs.data, aes(x=year, y=outlays, shape=)) + 
  geom_point(aes(shape=country))
# Call grid.arrange
grid.arrange(plot1, plot2, plot3, nrow=3, ncol=1)

Exporting

Two basic image types

Raster/Bitmap (.png, .jpeg)

Every pixel of a plot contains its own separate coding; not so great if you want to resize the image

jpeg(filename="example.png", width=, height=)
plot(x,y)
dev.off()

Vector (.pdf, .ps)

Every element of a plot is encoded with a function that gives its coding conditional on several factors; great for resizing

pdf(filename="example.pdf", width=, height=)
plot(x,y)
dev.off()

Exporting with lattice v. ggplot

# Assume we saved our plot is an object called example.plot

# lattice
trellis.device(device="pdf", filename="example.pdf")
print(example.plot)
dev.off()

# ggplot2
ggsave(filename="example.pdf", plot=example.plot, scale=, width=, height=) # ggplot2

Breakout

You’re welcome to try out either lattice or ggplot for these questions, but in the solutions we’ll focus on the ggplot approach.

For some of these you may want to use a smaller version of the dataset, such as a random subset, subset <- air[sample(1:nrow(air), 10000, replace = FALSE), ].

Basics

Plot a histogram of the flight delays with negative delays set to zero, censoring delay times at a maximum of 60 minutes.
Plot the arrival delay against the departure delay as a scatterplot.
Clean up your scatterplot with a title and axis labels. Output it as a PDF and see if you’d be comfortable with including it in a report/paper.
Make a boxplot of the departure delay as a function of the day of week.

Using the ideas

Create a trellis plot of departure delay boxplots, one per destination for this subset of destinations, DestSubset <- c('LAX','SEA','PHX','DEN','MSP','JFK','ATL','DFW','IAH', 'ORD'). Use a 2x5 layout of panels in the plot.
Subset the data to flights going to Chicago (ORD) and Houston (IAH). Plot arrival delay against scheduled departure time (CRSDepTime). Now plot so that flights to Chicago are in one color and those to Houston in another. Use scale_x_continuous() and scale_y_continuous() to set the x-axis limits to be in the range from 6 am to midnight and the y-axis limits to be in the range (-10, 120).

Advanced

Create a trellis plot where, for a given destination (see the subset in question 5), each panel uses a) hollow circles to plot departure delay as a function of time of day, and b) a red loess smoother without standard errors to plot the trend in departure delay over time of day. Limit the time of day shown to 6 am to midnight, and turn off the grey background. Figure out how to use partially-transparent points to reduce the effect of the overplotting of points.

R Bootcamp, Module 8: Graphics

August 2016, UC Berkeley

Kellie Ottoboni (kellieotto@berkeley.edu)

By way of introduction…

The data

Base plot

Other plot types

Object-oriented plots

Pros/cons

lattice and ggplot2

Basic usage: lattice

Basic usage: ggplot2

Comparing lattice and ggplot

lattice v. ggplot2: Densities

lattice v. ggplot2: X-Y scatter plots

lattice v. ggplot2: X-Y line plots

lattice v. ggplot2: bar plots

lattice v. ggplot2: box plots

lattice v. ggplot2: “trellis” plots

lattice v. ggplot2: countour plots

lattice v. ggplot2: tile/image/level plots

lattice: 3D plots

ggplot2: Panel plots

lattice v. ggplot2: options [labeling]

lattice v. ggplot2: options [axis + size scaling]

lattice v. ggplot2: options [graphical parameters]

ggplot2 and the Grammar of Graphics

ggplot2 and the Grammar of Graphics

Anatomy of aes()

Fitted lines and curves with ggplot2

lattice v. ggplot2: tables

Exporting

Exporting with lattice v. ggplot

Breakout

Basics

Using the ideas

Advanced